Publications

The northeast region of Bangladesh, a part of the mighty Meghna River system, experiences floods almost every year which cause severe damage to agriculture and property. For flood preparedness in this basin and planning of many development activities related to water resources, an accurate streamflow estimation is highly desired. Streamflow simulation, using conceptual models and physically based models is more common compared to the data-driven models such as artificial neural networks (ANNs). Recent studies show that data-driven methods can achieve comparable or even better performances than conventional hydrological models in streamflow simulation. However, the potential of ANN in flow simulation has not been studied much to simulate streamflow in the Meghna basin. This paper examines the utility of ANN in streamflow simulation and compares the results with the output from traditional hydrologic modeling. The SWAT (Soil and Water Assessment Tool) hydrologic model has been utilized for this purpose. The model was calibrated and validated using the gridded data from WFDE5 (WATCH Forcing Data methodology applied to ERA5 reanalysis data). On the other hand, ANN model was developed for the same basin utilizing the routinely available climate and soil moisture data from ECMWF. The ANN model was trained using the softmax activation function and Adam optimizer. The results indicate that both SWAT and ANN can efficiently simulate streamflow, but the former requires intensive spatial and temporal data sets, which are often not available with reasonable accuracy in a data-scarce region, like the Meghna basin. 

Remotely sensed precipitation data, though considered a viable alternative to gauge station readings in regions where data scarcity persists, often show considerable bias when compared to existing station-observed values over the Bangladesh region. For such instances, bias correction is required. Quantile mapping (QM) is a bias correction technique that has recently become popular in climate science. However, its efficacy in dealing with global rainfall data sets such as the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is yet to be determined. This study aims to correct the bias for daily precipitation from the CHIRPS (v2.0) dataset at 0.25° spatial resolution against existing station-observed rainfall provided by Bangladesh Meteorological Department (BMD), over a period of 2011-2021. The results of the study show that both the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods reduce the bias for the remotely sensed datasets by a considerable margin and perform better for the rainfall-heavy North-Eastern region as compared to other regions. A similar approach may help to reduce the bias of other remotely sensed precipitation products over Bangladesh, providing a more improved quality of data for further research.

Bangladesh is an extremely flood-prone country due to its geographical location at the downstream end of the Ganges, Brahmaputra and Meghna (GBM) river basin. Flood destroys agricultural products of large areas and causes loss of lives and damage to infrastructures. Heavy rainfall during the monsoon season is the major cause of flooding in this region which occurs almost every year. However, the lack of observations of rainfall in the upper catchment areas outside Bangladesh makes flood forecasting challenging in this region. In addition, errors in rainfall forecasts and lack of high-resolution bathymetry and topographic data put major constraints to flood forecasting in Bangladesh through hydrologic and hydrodynamic models. Currently Flood Forecasting and Warning Centre (FFWC) of Bangladesh Water Development Board (BWDB) is producing short-range flood forecasts with a lead time of up to three days. However, medium-range (3 to 5 days) forecasts are crucial for reducing flood-related losses as they provide more time for decision making and preparation compared to short-range forecasts. In this study, a flood forecast model based on Artificial Neural Network (ANN) has been developed for the Kushiyara river which is one of the major rivers of the northeastern region of Bangladesh. Rainfall data from the fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), daily Terrestrial Water Storage (TWS) from the Global Land Data Assimilation System with the Gravity Recovery and Climate Experiment Data Assimilation (GRACE-DA) and daily Surface Soil Moisture data from Soil Moisture Active Passive (SMAP) have been used as input to the model. The model shows reasonable accuracy in forecasting the water level of the Kushiyara river at Sheola station with a lead time of up to seven days. For 1-day lead time, the correlation coefficient (R) between the observed and simulated water levels is 0.97. The performance of the model is also promising for a medium-range forecast (R=0.93 for 7-day lead time). This study indicates that the release of daily GRACE gravity field solutions in near-real-time may enable us to forecast and monitor high volume flood events in this region. 

Soil moisture plays an essential role in the complex eco-hydrologic processes, such as infiltration, rainfall-evapotranspiration-runoff circulation, photosynthesis, and groundwater recharge. However, the accurate estimation of soil moisture (SM) at regional or larger scale is difficult because SM varies highly over space and time due to heterogeneous land cover and soil properties, and ground measurements are often time-consuming and expensive. Currently, Bangladesh Meteorological Department (BMD) measures SM only at twelve stations which is quite inadequate for assessing large-scale spatial and temporal variation of SM. Thus, satellite-derived soil moisture data products or Global Land Data Assimilation System simulated (GLDAS-2.2) soil moisture dataset with the Gravity Recovery and Climate Experiment Data Assimilation (GRACE-DA) can be promising alternatives to the in-situ measurement for this data-scarce region. In this study, the spatial and temporal variations of SM from GLDAS and Soil Moisture Active Passive (SMAP) satellite were compared against the in-situ measurements from seven agrometeorological stations of Bangladesh. The GLDAS and SMAP products overpredicted the in-situ SM for most of the stations and could capture the temporal dynamics of observed SM with correlation coefficient (R) of 0.36 and 0.17, respectively. Later an Artificial Neural Network model was developed based on soil moisture from both sources (SMAP and GLDAS) and terrestrial water storage from GLDAS to obtain more accurate estimation of SM for this data-scarce region. The ANN model shows an improvement in estimation and predicted SM with R = 0.63 (considering all stations). The results were more promising when separate model is developed for each study site. Incorporating additional climate data (such as precipitation with different lag times) as input improved the accuracy marginally. This study suggests that the release of daily GRACE gravity field solutions in near-real-time may provide a reasonable and continuous estimate of soil moisture in this data-scarce region. 

The Rohingya Refugee Crisis is now one of the massive humanitarian catastrophes witnessed around the globe. About a million people of the Rohingyas took shelter in the Refugee camps of Teknaf and the Ukhia Upazilas of Bangladesh since they fled from Myanmar on 25 August 2017, making it one of the largest refugee camps in the world. Owing to the sudden change in the number of inhabitants and rapid development of human settlement in the region, drastic changes in land use patterns need to be observed. But coarse resolution satellite images (≥ 30 m) often fail to depict the actual human settlements in the region accurately. In this study, high-resolution Google Earth Images were manually digitized to demarcate the Rohingya inhabited regions to generate more accurate Land-use patterns by the Rohingya refugees between 2017 and 2021. The obtained result was then compared with Land Cover Maps generated by Maximum Likelihood Supervised Classification technique (MLSC) in GIS software using Landsat 8 images. The study also shows that the camp settlement areas are gradually changing and have increased by 7 times in 5 years from May 2017, and the vegetative cover has shown a steep reduction (54%). The research also established a correlation between the numbers of refugees with the increasing human settlement area.